Building Smarter Integrations with CursorAI – Lessons from an integration implementation

Can an AI pair programmer actually help build a real-world integration? Here’s what we learned.

At CloudTalk, we’re big believers in testing new tools where it matters most: real code, real deadlines, and real unknowns. So when it came time to build a new integration with our partner — a recruiting CRM with a complex multi-step authentication flow — we gave CursorAI a starring role.

This blog shares our experience blending human logic with AI horsepower. Spoiler: It wasn’t magic. But it definitely made us smarter builders.

---

The Premise: Let Cursor Help Build the Integration

We approached this as a guided experiment:

“Give Cursor the partner’s API docs, show it our codebase (Integrations, Frontend & Backend), and ask it to build a new integration based on how we built others.”

What happened next was enlightening.

❌ Why the Full-AI Approach Didn’t Work

CursorAI had trouble with the end-to-end task for a few key reasons:

Authentication Was Too Unique

• The partner uses a Token multi-step auth flow.
• Cursor didn’t figure it out on its own, even when prompted.
• It missed that we had reusable OAuth components — they needed to be extended, not re-invented.

High-Level Logic Was Lost

• Cursor mimicked structure well, but didn’t understand business logic.
• It copied file structures, not workflows.
• The final flow was disjointed and incomplete.

It Assumed All APIs Work the Same

• If a endpoint is called /contacts it does not mean the same as partner’s /contacts endpoint.

Authentication Chains Broke It

• Login → bhRestToken → API calls? Too complex without line-by-line guidance.

Result: We had to rewrite large parts of the logic. It simply couldn’t own the whole job.

---

But Here’s Where Cursor Absolutely Shined

While it fumbled the big picture, CursorAI crushed it on smaller, atomic tasks:

✅ GraphQL Boilerplate

• Built queries, mutations, hooks, and types super fast
• Worked brilliantly with our dashboard UI setup
• Understood TypeScript and React patterns well

✅ Backend Helpers

• Formatting timestamps, filtering objects, mapping APIs to DTOs
• Clean, reusable utility functions

✅ Pattern-Based Refactoring

• Retry logic
• Error handling
• Request body shaping

✅ Unit Test Generation

• Especially effective for isolated functions with predictable input/output

✅ Incremental Extensions

• Once a working export logic existed, Cursor helped extend it quickly:

• Add tags
• Log call notes
• Map additional fields

Estimate: These smaller tasks were 3× faster using Cursor.

---

Real Lessons from Real Integration Work

✍️ Atomic Tasks Win

“One file, one goal, one prompt.”

Cursor works best when given clear boundaries. If you ask it to handle too much, it’ll hallucinate structure, over-engineer solutions, or miss context entirely.

📂 Reference Files Matter

Open related files before prompting. Cursor understands local context and nearby code better than global explanations.

🪤 Be Wary of Its Imagination

“Sometimes it made weird, completely unhelpful changes.”

This included editing unrelated files, creating new folders, or trying to “fix” unrelated lint errors.

Tip: Set boundaries like:

• “⚠️ Don’t change anything unless explicitly asked.”
• “Ignore ESLint warnings.”
• “No commits unless prompted.”

🧠 Cursor ≠ Architect

“It doesn’t understand your business. You do.”

When tasks required understanding the actual why, Cursor flopped. Complex auth flows, entity relationships, and real-world business logic still need a human in the driver’s seat.

---

How We Adapted Our Workflow

🧱 Break Down the Work

Instead of one massive prompt, we split work into subtasks. Also what is essential:

• A precise prompt
• File references
• Input/output expectations
• Constraints (auth, templates, fallbacks)

This structure mirrored how we’d brief a junior developer — and Cursor responded well.

🔁 Reuse Patterns from Past Work

Cursor excels at copying and adapting known patterns. We pointed it to an existing integration as a reference, and it reused retry logic, field mappings, and entity handling neatly.

🧪 Prompt Like You Mean It

• Start with the action
• Mention file/function names
• Add expected inputs/outputs
• Specify test requirements
• Reuse existing logic wherever possible

This helped us move from vague requests to executable outcomes.

---

Lessons from First-Time Adoption (Beyond the Integration Itself)

Our broader takeaways on using CursorAI across an existing project:

⚖️ Know When to Take Over

Sometimes, writing the first draft manually is faster. Then you can use Cursor to clean up, extend, or comment the code.

💥 Don’t Be Afraid to Revert

If Cursor’s changes go off the rails, reset. A quick git checkout beats debugging a broken AI logic chain.

🔍 Ask Before You Trust

“Cursor, how would you implement this?”

Start with explanation prompts before execution prompts. It helps you gauge whether it actually understands the goal.

📉 The Productivity Multiplier is Real — but Contextual

We didn’t see 3×–4× improvement across the board. But for the right tasks? Cursor absolutely saved hours. Across the entire integration, we estimate:

• 1.5×–2× overall speedup
• 3×–4× speedup on repetitive tasks
• 0× (or negative!) on complex logic and multi-entity orchestration

---

The Verdict: A Sharp Tool, Not a Silver Bullet

CursorAI works best as a junior co-pilot:

• Great with patterns, bad with puzzles
• Amazing for snippets, terrible for systems
• Quick at repetition, poor at orchestration

If you treat it like a magic senior dev, you’ll get frustrated.
If you treat it like a coachable assistant? You’ll be impressed.

Next up: we’re planning to apply these learnings to our upcoming Odoo integration and track how much we improve by applying what we now know.

Spoiler: AI won’t replace us. But it definitely helps us build better — and faster.

---

Let me know if you’d like this post formatted for CMS, Medium, or internal wiki, or if you’d like social snippets or visuals created to promote it!